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We aimed to assess whether individuals with type 2 diabetes (T2D) have increased risk of vertebral fractures (VFs) and to estimate nonvertebral fracture and mortality risk among individuals with both prevalent T2D and VFs. RESEARCH DESIGN AND METHODSA systematic PubMed search was performed to identify studies that investigated the relationship between T2D and VFs. Cohorts providing individual participant data (IPD) were also included. Estimates from published summary data and IPD cohorts were pooled in a random-effects meta-analysis. Multivariate Cox regression models were used to estimate nonvertebral fracture and mortality risk among individuals with T2D and VFs. RESULTSAcross 15 studies comprising 852,705 men and women, individuals with T2D had lower risk of prevalent (odds ratio [OR] 0.84 [95% CI 0.74-0.95]; I 2 5 0.0%; P het 5 0.54) but increased risk of incident VFs (OR 1.35 [95% CI 1.27-1.44]; I 2 5 0.6%; P het 5 0.43). In the IPD cohorts (N 5 19,820), risk of nonvertebral fractures was higher in those with both T2D and VFs compared with those without T2D or VFs (hazard ratio [HR] 2.42 [95% CI 1.86-3.15]) or with VFs (HR 1.73 [95% CI 1.32-2.27]) or T2D (HR 1.94 [95% CI 1.46-2.59]) alone. Individuals with both T2D and VFs had increased mortality compared with individuals without T2D and VFs (HR 2.11 [95% CI 1.72-2.59]) or with VFs alone ) and borderline increased compared with individuals with T2D alone (HR 1.23 [95% CI 0.99-1.52]). CONCLUSIONSBased on our findings, individuals with T2D should be systematically assessed for presence of VFs, and, as in individuals without T2D, their presence constitutes an indication to start osteoporosis treatment for the prevention of future fractures.Type 2 diabetes (T2D) is a chronic metabolic disease characterized by several complications such as cardiovascular disease, neuropathy, nephropathy, retinopathy, and mortality (1). Moreover, skeletal complications are also evident, as individuals with T2D present increased risk of hip and nonvertebral fractures compared with the general population, despite similar or higher levels of areal bone mineral density (BMD). This suggests that BMD underestimates risk of fracture in these individuals (2,3). Several mechanisms have been suggested to explain this increased fracture risk:

Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high‐dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12‐point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML‐based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model‐based decision‐making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).

Regional soft tissue may have a noise effect on trabecular bone score (TBS) and eventually alter its estimate. The current TBS software (TBS iNsight®) is based on an algorithm accounting for body mass index (BMI) (TBSv3.03). We aimed to explore the updated TBS algorithm that accounts for soft tissue thickness (TBSv4.0). This study was embedded in the OsteoLaus cohort of women in Lausanne, Switzerland. Hip and lumbar spine (LS) dual‐energy X‐ray absorptiometry (DXA) scans were performed using Discovery A System (Hologic). The incident major osteoporotic fractures (MOFs) were assessed from vertebral fracture assessments using Genant's method (vertebral MOF) or questionnaires (nonvertebral MOF). We assessed the correlations of bone mineral density (BMD) or TBS with body composition parameters; MOF prediction ability of both versions of TBS; and the differences between Fracture Risk Assessment Tool (FRAX) adjusted for TBSv3.03 or TBSv4.0. In total, 1362 women with mean ± SD age 64.4 ± 7.5 years and mean ± SD BMI 25.9 ± 4.5 kg/m2 were followed for 4.4 years and 132 experienced an MOF. All the anthropometric measurements of our interest were positively correlated with LS, femoral neck, or hip BMD and TBSv4.0; whereas with TBSv3.03 their correlations were negative. In the models adjusted for age, soft tissue thickness, osteoporotic treatment, and LS‐BMD, for each SD decline in TBSv3.03, there was a 43% (OR 1.43; 95% CI, 1.12 to 1.83) increase in the odds of having MOF; whereas for each SD decline in TBSv4.0, there was a 54% (OR 1.54; 95% CI, 1.18 to 2.00) increase in the odds of having an MOF. Both FRAXs were very strongly correlated and the mild differences were present in the already high‐risk women for MOF. This study shows that TBSv4.0 overcomes the debatable residual negative correlation of the current TBS with body size and composition parameters, postulating itself as free from the previously acknowledged technical limitation of TBS. © 2019 American Society for Bone and Mineral Research.